Noninvasive urinary metabonomic diagnosis of human bladder cancer

Global gas chromatography/time-of-flight mass spectrometry (GC/TOFMS)-based metabonomic profiling of lyophilized human feces

Gas chromatography mass spectrometry (GC/MS)-based fecal metabonomics represents a powerful systems biology approach for elucidating metabolic biomarkers of lower gastrointestinal tract (GIT) diseases. Unlike metabolic profiling of fecal water, the profiling of complete fecal material remains under-explored. Here, a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) method was developed and validated for the global metabonomic profiling of human feces. Fecal and fecal water metabotypes were also profiled and compared. Additionally, the unclear influence of blood in stool on the fecal metabotype was investigated unprecedentedly. Eighty milligram of lyophilized feces was ultrasonicated with 1mL of methanol:water (8:2) for 30min, followed by centrifugation, drying of supernatant, oximation and trimethylsilylation for 45min. Lyophilized feces demonstrated a more comprehensive metabolic coverage than fecal water, based on the number of chromatographic peaks. Principal component analysis (PCA) indicated occult blood (1mgHb/g feces) exerted a negligible effect on the fecal metabotype. Conversely, a unique metabotype related to feces spiked with gross blood (100mgHb/g feces) was revealed (PCA, R(2)X=0.837, Q(2)=0.794), confirming the potential confounding effect of gross GIT bleeding on the fecal metabotype. This pertinent finding highlights the importance of prudent interpretation of fecal metabonomic data, particularly in GIT diseases where bleeding is prevalent.

Urinary metabotyping of bladder cancer using two-dimensional gas chromatography time-of-flight mass spectrometry

Cystoscopy is the gold standard clinical diagnosis of human bladder cancer (BC). As cystoscopy is expensive and invasive, it compromises patients' compliance toward surveillance screening and challenges the detection of recurrent BC. Therefore, the development of a noninvasive method for the diagnosis and surveillance of BC and the elucidation of BC progression become pertinent. In this study, urine samples from 38 BC patients and 61 non-BC controls were subjected to urinary metabotyping using two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS). Subsequent to data preprocessing and chemometric analysis, the orthogonal partial least-squares discriminant analysis (OPLS-DA, R2X=0.278, R2Y=0.904 and Q2Y (cumulative)=0.398) model was validated using permutation tests and receiver operating characteristic (ROC) analysis. Marker metabolites were further screened from the OPLS-DA model using statistical tests. GC×GC-TOFMS urinary metabotyping demonstrated 100% specificity and 71% sensitivity in detecting BC, while 100% specificity and 46% sensitivity were observed via cytology. In addition, the model revealed 46 metabolites that characterize human BC. Among the perturbed metabolic pathways, our clinical finding on the alteration of the tryptophan-quinolinic metabolic axis in BC suggested the potential roles of kynurenine in the malignancy and therapy of BC. In conclusion, global urinary metabotyping holds potential for the noninvasive diagnosis and surveillance of BC in clinics. In addition, perturbed metabolic pathways gleaned from urinary metabotyping shed new and established insights on the biology of human BC.

Clinical metabolomics paves the way towards future healthcare strategies

Metabolomics is recognized as a powerful top-down system biological approach to understand genetic-environment-health paradigms paving new avenues to identify clinically relevant biomarkers. It is nowadays commonly used in clinical applications shedding new light on physiological regulatory processes of complex mammalian systems with regard to disease aetiology, diagnostic stratification and, potentially, mechanism of action of therapeutic solutions. A key feature of metabolomics lies in its ability to underpin the complex metabolic interactions of the host with its commensal microbial partners providing a new way to define individual and population phenotypes. This review aims at describing recent applications of metabolomics in clinical fields with insight into diseases, diagnostics/monitoring and improvement of homeostatic metabolic regulation.

HR-MAS NMR tissue metabolomic signatures cross-validated by mass spectrometry distinguish bladder cancer from benign disease

Effective diagnosis and surveillance of bladder cancer (BCa) is currently challenged by detection methods that are of poor sensitivity, particularly for low-grade tumors, resulting in unnecessary invasive procedures and economic burden. We performed HR-MAS NMR-based global metabolomic profiling and applied unsupervised principal component analysis (PCA) and hierarchical clustering performed on NMR data set of bladder-derived tissues and identified metabolic signatures that differentiate BCa from benign disease. A partial least-squares discriminant analysis (PLS-DA) model (leave-one-out cross-validation) was used as a diagnostic model to distinguish benign and BCa tissues. Receiver operating characteristic curve generated either from PC1 loadings of PCA or from predicted Y-values resulted in an area under curve of 0.97. Relative quantification of more than 15 tissue metabolites derived from HR-MAS NMR showed significant differences (P < 0.001) between benign and BCa samples. Noticeably, striking metabolic signatures were observed even for early stage BCa tissues (Ta-T1), demonstrating the sensitivity in detecting BCa. With the goal of cross-validating metabolic signatures derived from HR-MAS NMR, we utilized the same tissue samples to analyze 8 metabolites through gas chromatography-mass spectrometry (GC-MS)-targeted analysis, which undoubtedly complements HR-MAS NMR-derived metabolomic information. Cross-validation through GC-MS clearly demonstrates the utility of a straightforward, nondestructive, and rapid HR-MAS NMR technique for clinical diagnosis of BCa with even greater sensitivity. In addition to its utility as a diagnostic tool, these studies will lead to a better understanding of aberrant metabolic pathways in cancer as well as the design and implementation of personalized cancer therapy through metabolic modulation.

Metabolomic changes in follicular fluid induced by soy isoflavones administered to rats from weaning until sexual maturity

Female Wistar rats at 21 days of age were treated with one of three concentrations of soy isoflavones (SIF) (50, 100 or 200mg/kg body weight, orally, once per day) from weaning until sexual maturity (3 months) in order to evaluate the influence of SIF on ovarian follicle development. After treatment, the serum sex hormone levels and enumeration of ovarian follicles of the ovary were measured. The metabolic profile of follicular fluid was determined using HPLC-MS. Principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) was used to identify differences in metabolites and reveal useful toxic biomarkers. The results indicated that modest doses of SIF affect ovarian follicle development, as demonstrated by decreased serum estradiol levels and increases in both ovarian follicle atresia and corpora lutea number in the ovary. SIF treatment-related metabolic alterations in follicular fluid were also found in the PCA and PLS-DA models. The 24 most significantly altered metabolites were identified, including primary sex hormones, amino acids, fatty acids and metabolites involved in energy metabolism. These findings may indicate that soy isoflavones affect ovarian follicle development by inducing metabolomic variations in the follicular fluid.

Calcium-deficiency assessment and biomarker identification by an integrated urinary metabonomics analysis

BACKGROUND

Calcium deficiency is a global public-health problem. Although the initial stage of calcium deficiency can lead to metabolic alterations or potential pathological changes, calcium deficiency is difficult to diagnose accurately. Moreover, the details of the molecular mechanism of calcium deficiency remain somewhat elusive. To accurately assess and provide appropriate nutritional intervention, we carried out a global analysis of metabolic alterations in response to calcium deficiency.

METHODS

The metabolic alterations associated with calcium deficiency were first investigated in a rat model, using urinary metabonomics based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry and multivariate statistical analysis. Correlations between dietary calcium intake and the biomarkers identified from the rat model were further analyzed to confirm the potential application of these biomarkers in humans.

RESULTS

Urinary metabolic-profiling analysis could preliminarily distinguish between calcium-deficient and non-deficient rats after a 2-week low-calcium diet. We established an integrated metabonomics strategy for identifying reliable biomarkers of calcium deficiency using a time-course analysis of discriminating metabolites in a low-calcium diet experiment, repeating the low-calcium diet experiment and performing a calcium-supplement experiment. In total, 27 biomarkers were identified, including glycine, oxoglutaric acid, pyrophosphoric acid, sebacic acid, pseudouridine, indoxyl sulfate, taurine, and phenylacetylglycine. The integrated urinary metabonomics analysis, which combined biomarkers with regular trends of change (types A, B, and C), could accurately assess calcium-deficient rats at different stages and clarify the dynamic pathophysiological changes and molecular mechanism of calcium deficiency in detail. Significant correlations between calcium intake and two biomarkers, pseudouridine (Pearson correlation, r = 0.53, P = 0.0001) and citrate (Pearson correlation, r = -0.43, P = 0.001), were further confirmed in 70 women.

CONCLUSIONS

To our knowledge, this is the first report of reliable biomarkers of calcium deficiency, which were identified using an integrated strategy. The identified biomarkers give new insights into the pathophysiological changes and molecular mechanisms of calcium deficiency. The correlations between calcium intake and two of the biomarkers provide a rationale or potential for further assessment and elucidation of the metabolic responses of calcium deficiency in humans.

Metabolic signatures of esophageal cancer: NMR-based metabolomics and UHPLC-based focused metabolomics of blood serum

Focused metabolic profiling is a powerful tool for the determination of biomarkers. Here, a more global proton nuclear magnetic resonance ((1)H NMR)-based metabolomic approach coupled with a relative simple ultra high performance liquid chromatography (UHPLC)-based focused metabolomic approach was developed and compared to characterize the systemic metabolic disturbances underlying esophageal cancer (EC) and identify possible early biomarkers for clinical prognosis. Serum metabolic profiling of patients with EC (n=25) and healthy controls (n=25) was performed by using both (1)H NMR and UHPLC, and metabolite identification was achieved by multivariate statistical analysis. Using orthogonal projection to least squares discriminant analysis (OPLS-DA), we could distinguish EC patients from healthy controls. The predictive power of the model derived from the UHPLC-based focused metabolomics performed better in both sensitivity and specificity than the results from the NMR-based metabolomics, suggesting that the focused metabolomic technique may be of advantage in the future for the determination of biomarkers. Moreover, focused metabolic profiling is highly simple, accurate and specific, and should prove equally valuable in metabolomic research applications. A total of nineteen significantly altered metabolites were identified as the potential disease associated biomarkers. Significant changes in lipid metabolism, amino acid metabolism, glycolysis, ketogenesis, tricarboxylic acid (TCA) cycle and energy metabolism were observed in EC patients compared with the healthy controls. These results demonstrated that metabolic profiling of serum could be useful as a screening tool for early EC diagnosis and prognosis, and might enhance our understanding of the mechanisms involved in the tumor progression.

The significance and robustness of a plasma free amino acid (PFAA) profile-based multiplex function for detecting lung cancer

Background

We have recently reported on the changes in plasma free amino acid (PFAA) profiles in lung cancer patients and the efficacy of a PFAA-based, multivariate discrimination index for the early detection of lung cancer. In this study, we aimed to verify the usefulness and robustness of PFAA profiling for detecting lung cancer using new test samples.

Methods

Plasma samples were collected from 171 lung cancer patients and 3849 controls without apparent cancer. PFAA levels were measured by high-performance liquid chromatography (HPLC)–electrospray ionization (ESI)–mass spectrometry (MS).

Results

High reproducibility was observed for both the change in the PFAA profiles in the lung cancer patients and the discriminating performance for lung cancer patients compared to previously reported results. Furthermore, multivariate discriminating functions obtained in previous studies clearly distinguished the lung cancer patients from the controls based on the area under the receiver-operator characteristics curve (AUC of ROC = 0.731 ~ 0.806), strongly suggesting the robustness of the methodology for clinical use. Moreover, the results suggested that the combinatorial use of this classifier and tumor markers improves the clinical performance of tumor markers.

Conclusions

These findings suggest that PFAA profiling, which involves a relatively simple plasma assay and imposes a low physical burden on subjects, has great potential for improving early detection of lung cancer.

Molecular diagnostic trends in urological cancer: biomarkers for non-invasive diagnosis

The early detection of urological cancers is pivotal for successful patient treatment and management. The development of molecular assays that can diagnose disease accurately, or that can augment current methods of evaluation, would be a significant advance. Ideally, such molecular assays would be applicable to non-invasively obtained body fluids, enabling not only diagnosis of at risk patients, but also asymptomatic screening, monitoring disease recurrence and response to treatment. The advent of advanced proteomics and genomics technologies and associated bioinformatics development is bringing these goals into focus. In this article we will discuss the promise of biomarkers in urinalysis for the detection and clinical evaluation of the major urological cancers, including bladder, kidney and prostate. The development of urine-based tests to detect urological cancers would be of tremendous benefit to both patients and the healthcare system.

Advances in gas chromatographic methods for the identification of biomarkers in cancer

Screening complex biological specimens such as exhaled air, tissue, blood and urine to identify biomarkers in different forms of cancer has become increasingly popular over the last decade, mainly due to new instruments and improved bioinformatics. However, despite some progress, the identification of biomarkers has shown to be a difficult task with few new biomarkers (excluding recent genetic markers) being considered for introduction to clinical analysis. This review describes recent advances in gas chromatographic methods for the identification of biomarkers in the detection, diagnosis and treatment of cancer. It presents a general overview of cancer metabolism, the current biomarkers used for cancer diagnosis and treatment, a background to metabolic changes in tumors, an overview of current GC methods, and collectively presents the scope and outlook of GC methods in oncology.

Recent advances in metabolomics in oncology

The burden of cancer is growing worldwide and with it a more desperate need for better tools to detect, diagnose and monitor the disease is required. It is well recognized that cancer cells are characterized by distinct metabolic perturbations. The metabolomics approach involves the comprehensive profiling of the full complement of low MW compounds in a biological system. By applying advanced analytical and statistical tools, the 'metabolome' is mined for biomarkers that are associated with the state of cancer. This review presents an introduction to the main analytical platforms used in metabolomics analyses, such as NMR spectroscopy and MS, as well as the statistical tools used to mine these datasets. The discussion focuses on 'state-of-the-art' investigations on the four cancer types that have received the most study by metabolomics, namely breast, prostate, colorectal and liver cancer.

Metabolomics and bladder cancer

Diagnosis of bladder cancer is primarily made based on clinical presentation and then by direct visualization with cystoscopy. Despite the massive investments recently made to identify urinary-based assays that are able to diagnosis urothelial carcinoma, urine cytology and cystoscopy still remain the gold standard. Recently proof of principle studies have shown that noninvasive urine-based metabolomics, using high pressure liquid chromatography (HPLC) and nuclear magnetic resonance (NMR), may be able to accurately diagnosis bladder cancer. This review discusses the published studies investigating metabolomics and bladder cancer and the future potential of this developing field.

Plasma metabonomics as a novel diagnostic approach for major depressive disorder

Major depressive disorder (MDD) is a socially detrimental psychiatric disorder, contributing to increased healthcare expenditures and suicide rates. However, no empirical laboratory-based tests are available to support the diagnosis of MDD. In this study, a NMR-based plasma metabonomic method for the diagnosis of MDD was tested. Proton nuclear magnetic resonance ((1)H NMR) spectra of plasma sampled from first-episode drug-naı̈ve depressed patients (n = 58) and healthy controls (n = 42) were recorded and analyzed by orthogonal partial least-squares discriminant analysis (OPLS-DA). The OPLS-DA score plots of the spectra demonstrated that the depressed patient group was significantly distinguishable from the healthy control group. Moreover, the method accurately diagnosed blinded samples (n = 26) in an independent replication cohort with a sensitivity and specificity of 92.8% and 83.3%, respectively. Taken together, NMR-based plasma metabonomics may offer an accurate empirical laboratory-based method applicable to the diagnosis of MDD.

Applications of mass spectrometry to metabolomics and metabonomics: detection of biomarkers of aging and of age-related diseases

Every 5 years or so new technologies, or new combinations of old ones, seemingly burst onto the science scene and are then sought after until they reach the point of becoming commonplace. Advances in mass spectrometry instrumentation, coupled with the establishment of standardized chemical fragmentation libraries, increased computing power, novel data-analysis algorithms, new scientific applications, and commercial prospects have made mass spectrometry-based metabolomics the latest sought-after technology. This methodology affords the ability to dynamically catalogue and quantify, in parallel, femtomole quantities of cellular metabolites. The study of aging, and the diseases that accompany it, has accelerated significantly in the last decade. Mutant genes that alter the rate of aging have been found that increase lifespan by up to 10-fold in some model organisms, and substantial progress has been made in understanding fundamental alterations that occur at both the mRNA and protein level in tissues of aging organisms. The application of metabolomics to aging research is still relatively new, but has already added significant insight into the aging process. In this review we summarize these findings. We have targeted our manuscript to two audiences: mass spectrometrists interested in applying their technical knowledge to unanswered questions in the aging field, and gerontologists interested in expanding their knowledge of both mass spectrometry and the most recent advances in aging-related metabolomics.

Metabolomic profiling reveals potential markers and bioprocesses altered in bladder cancer progression

Although alterations in xenobiotic metabolism are considered causal in the development of bladder cancer, the precise mechanisms involved are poorly understood. In this study, we used high-throughput mass spectrometry to measure over 2,000 compounds in 58 clinical specimens, identifying 35 metabolites which exhibited significant changes in bladder cancer. This metabolic signature distinguished both normal and benign bladder from bladder cancer. Exploratory analyses of this metabolomic signature in urine showed promise in distinguishing bladder cancer from controls and also nonmuscle from muscle-invasive bladder cancer. Subsequent enrichment-based bioprocess mapping revealed alterations in phase I/II metabolism and suggested a possible role for DNA methylation in perturbing xenobiotic metabolism in bladder cancer. In particular, we validated tumor-associated hypermethylation in the cytochrome P450 1A1 (CYP1A1) and cytochrome P450 1B1 (CYP1B1) promoters of bladder cancer tissues by bisulfite sequence analysis and methylation-specific PCR and also by in vitro treatment of T-24 bladder cancer cell line with the DNA demethylating agent 5-aza-2'-deoxycytidine. Furthermore, we showed that expression of CYP1A1 and CYP1B1 was reduced significantly in an independent cohort of bladder cancer specimens compared with matched benign adjacent tissues. In summary, our findings identified candidate diagnostic and prognostic markers and highlighted mechanisms associated with the silencing of xenobiotic metabolism. The metabolomic signature we describe offers potential as a urinary biomarker for early detection and staging of bladder cancer, highlighting the utility of evaluating metabolomic profiles of cancer to gain insights into bioprocesses perturbed during tumor development and progression.

Plasma free amino acid profiling of five types of cancer patients and its application for early detection

Background

Recently, rapid advances have been made in metabolomics-based, easy-to-use early cancer detection methods using blood samples. Among metabolites, profiling of plasma free amino acids (PFAAs) is a promising approach because PFAAs link all organ systems and have important roles in metabolism. Furthermore, PFAA profiles are known to be influenced by specific diseases, including cancers. Therefore, the purpose of the present study was to determine the characteristics of the PFAA profiles in cancer patients and the possibility of using this information for early detection.

Methods and Findings

Plasma samples were collected from approximately 200 patients from multiple institutes, each diagnosed with one of the following five types of cancer: lung, gastric, colorectal, breast, or prostate cancer. Patients were compared to gender- and age- matched controls also used in this study. The PFAA levels were measured using high-performance liquid chromatography (HPLC)–electrospray ionization (ESI)–mass spectrometry (MS). Univariate analysis revealed significant differences in the PFAA profiles between the controls and the patients with any of the five types of cancer listed above, even those with asymptomatic early-stage disease. Furthermore, multivariate analysis clearly discriminated the cancer patients from the controls in terms of the area under the receiver-operator characteristics curve (AUC of ROC >0.75 for each cancer), regardless of cancer stage. Because this study was designed as case-control study, further investigations, including model construction and validation using cohorts with larger sample sizes, are necessary to determine the usefulness of PFAA profiling.

Conclusions

These findings suggest that PFAA profiling has great potential for improving cancer screening and diagnosis and understanding disease pathogenesis. PFAA profiles can also be used to determine various disease diagnoses from a single blood sample, which involves a relatively simple plasma assay and imposes a lower physical burden on subjects when compared to existing screening methods.

Efficiency of a miniaturized silica monolithic cartridge in reducing matrix ions as demonstrated in the simultaneous extraction of morphine and codeine from urine samples for quantification with liquid chromatography-tandem mass spectrometry (LC-MS/MS)

Presence of matrix ions could negatively affect the sensitivity and selectivity of liquid chromatography-tandem mass spectrometer (LC-MS/MS). In this study, the efficiency of a miniaturized silica monolithic cartridge in reducing matrix ions was demonstrated in the simultaneous extraction of morphine and codeine from urine samples for quantification with LC-MS. The miniaturized silica monolith with hydroxyl groups present on the largely exposed surface area function as a weak cation exchanger for solid phase extraction (SPE). The miniaturized silica cartridge in 1 cm diameter and 0.5 cm length was housed in a 2-ml syringe fixed over a SPE vacuum manifold for extraction. The cleaning effectiveness of the cartridge was confirmed by osmometer, atomic absorption spectrometer, LC-MS and GC-TOFMS. The drugs were efficiently extracted from urine samples with recoveries ranging from 86% to 114%. The extracted analytes, after concentration and reconstitution, were quantified using LC-MS/MS. The limits of detection for morphine and codeine were 2 ng/ml and 1 ng/mL, respectively. The relative standard deviations of measurements ranged from 3% to 12%. The monolithic sorbent offered good linearity with correlation coefficients > 0.99, over a concentration range of 50-500 ng/ml. The silica monolithic cartridge was found to be more robust than the particle-based packed sorbent and also the commercial cartridge with regards to its recyclability and repeated usage with minimal loss in efficiency. Our study demonstrated the efficiency of the miniaturized silica monolith for removal of matrix ions and extraction of drugs of abuse in urinary screening.

Bladder cancer determination via two urinary metabolites: a biomarker pattern approach

The purpose of this study was to use metabonomic profiling to identify a potential specific biomarker pattern in urine as a noninvasive bladder cancer (BC) detection strategy. A liquid chromatography-mass spectrometry based method, which utilized both reversed phase liquid chromatography and hydrophilic interaction chromatography separations, was performed, followed by multivariate data analysis to discriminate the global urine profiles of 27 BC patients and 32 healthy controls. Data from both columns were combined, and this combination proved to be effective and reliable for partial least squares-discriminant analysis. Following a critical selection criterion, several metabolites showing significant differences in expression levels were detected. Receiver operating characteristic analysis was used for the evaluation of potential biomarkers. Carnitine C9:1 and component I, were combined as a biomarker pattern, with a sensitivity and specificity up to 92.6% and 96.9%, respectively, for all patients and 90.5% and 96.9%, respectively for low-grade BC patients. Metabolic pathways of component I and carnitine C9:1 are discussed. These results indicate that metabonomics is a practicable tool for BC diagnosis given its high efficacy and economization. The combined biomarker pattern showed better performance than single metabolite in discriminating bladder cancer patients, especially low-grade BC patients, from healthy controls.

Metabolomics study of alcohol-induced liver injury and hepatocellular carcinoma xenografts in mice

Alcohol abuse is one of the major causes of liver injury and a promoter for hepatocellular carcinoma (HCC). To understand the disease-associated metabolic changes, we investigated and compared the profiles of metabolites in nude mice with alcohol-induced liver injury or bearing a HCC xenograft (HCCX). Alcohol-induced liver injury was achieved by daily administration of grain liquor, and HCC xenografts were generated by subcutaneous inoculation of HepG2 cells in nude mice. Metabolites in serum samples were profiled by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS). The acquired data was analyzed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to identify potential disease-specific biomarkers. Results showed that the phosphatidylcholine (PC) levels were significantly higher in both liver injury and HCCX mice compared with the control. Interestingly, lysophosphatidylcholines (LPCs) that contain saturated or monounsaturated fatty acids were reduced in both liver injury and HCCX mice, but polyunsaturated fatty acids LPCs were elevated in liver injury mice only. These data delineated the disease-related metabolic alterations of LPCs in liver injury and HCC, suggesting that the LPC profile in serum may be biomarkers for these two common liver diseases.

Cancer biomarker discovery: Opportunities and pitfalls in analytical methods

Many diseases result in specific and characteristic changes in the chemical and biochemical profiles of biological fluids and tissues prior to development of clinical symptoms. These changes are often useful diagnostic and prognostic biomarkers. Identifying biomarkers that can be used for the early detection of cancer will result in more efficient treatments, reduction in suffering, and lower mortality rates. An ideal screening test should be non-invasive with high sensitivity and specificity. Proteomic and metabolomic analyses of biological samples can reveal changes in abundance levels of metabolites and proteins that when validated and confirmed through clinical trials can function as clinical tests for early detection, diagnosis, monitoring disease progression, and predicting therapeutic response. While the past decade has seen great advancements in proteomics and metabolomics research producing potential biomarkers for cancer, most of the identified biomarkers have failed to replace existing clinical tests. To become a clinically approved test, a potential biomarker should be confirmed and validated using hundreds of specimens and should be reproducible, specific, and sensitive. A search of the scientific and medical literature indicates that many studies report the discovery of potential biomarkers without proper validation and/or they do not meet the above criteria. In this manuscript, we will discuss the successes and the pitfalls of biomarker research and comment on study and experimental design, which in most cases is lacking, resulting in suboptimal biomarkers.

LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery

A LC-MS based method, which utilizes both reversed-performance (RP) chromatography and hydrophilic interaction chromatography (HILIC) separations, has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of renal cell carcinoma (RCC) patients and healthy controls. The HILIC was found necessary for a comprehensive serum metabonomic profiling as well as RP separation. The feasibility of using serum metabonomics for the diagnosis and staging of RCC has been evaluated. One-hundred percent sensitivity in detection has been achieved, and a satisfactory clustering between the early stage and advanced-stage patients is observed. The results suggest that the combination of LC-MS analysis with multivariate statistical analysis can be used for RCC diagnosis and has potential in the staging of RCC. The MS/MS experiments have been carried out to identify the biomarker patterns that made great contribution to the discrimination. As a result, 30 potential biomarkers for RCC are identified. It is possible that the current biomarker patterns are not unique to RCC but just the result of any malignancy disease. To further elucidate the pathophysiology of RCC, related metabolic pathways have been studied. RCC is found to be closely related to disturbed phospholipid catabolism, sphingolipid metabolism, phenylalanine metabolism, tryptophan metabolism, fatty acid beta-oxidation, cholesterol metabolism, and arachidonic acid metabolism.

Possibility of multivariate function composed of plasma amino acid profiles as a novel screening index for non-small cell lung cancer: a case control study

Background

The amino-acid balance in cancer patients often differs from that in healthy individuals, because of metabolic changes. This study investigated the use of plasma amino-acid profiles as a novel marker for screening non-small-cell lung cancer (NSCLC) patients.

Methods

The amino-acid concentrations in venous blood samples from pre-treatment NSCLC patients (n = 141), and age-matched, gender-matched, and smoking status-matched controls (n = 423), were measured using liquid chromatography and mass spectrometry. The resultant study data set was subjected to multiple logistic regression analysis to identify amino acids related with NSCLC and construct the criteria for discriminating NSCLC patients from controls. A test data set derived from 162 patients and 3,917 controls was used to validate the stability of the constructed criteria.

Results

The plasma amino-acid profiles significantly differed between the NSCLC patients and the controls. The obtained model (including alanine, valine, isoleucine, histidine, tryptophan and ornithine concentrations) performed well, with an area under the curve of the receiver-operator characteristic curve (ROC_AUC) of >0.8, and allowed NSCLC patients and controls to be discriminated regardless of disease stage or histological type.

Conclusions

This study shows that plasma amino acid profiling will be a potential screening tool for NSCLC.

Comparative evaluation of software for retention time alignment of gas chromatography/time-of-flight mass spectrometry-based metabonomic data

In chromatography-based metabonomic research, retention time (RT) alignment of chromatographic peaks poses a challenge for the accurate profiling of biomarkers. Although a number of RT alignment software has been reported, the performance of these software packages have not been comprehensively evaluated. This study aimed to evaluate the RT alignment accuracy of publicly available and commercial RT alignment software. Two gas chromatography/mass spectrometry (GC/MS) datasets acquired from a mixture of standard metabolites and human bladder cancer urine samples, were used to assess three publicly available software packages, MetAlign, MZmine and TagFinder, and two commercial applications comprising the Calibration feature and Statistical Compare of ChromaTOF software. The overall RT alignment accuracies in aligning standard compounds mixture were 93, 92, 74, 73 and 42% for Calibration feature, MZmine, MetAlign, Statistical Compare and TagFinder, respectively. Additionally, unique trends were observed for the individual software with regards to the different experimental conditions related to extent and direction of RT shifts. Conflicting performance was observed for human urine samples suggesting that RT misalignments still occurred despite the use of RT alignment software. While RT alignment remains an inevitable step in data preprocessing, metabonomic researchers are recommended to perform manual check on the RT alignment of important biomarkers as part of their validation process.

Metabolomics study of human urinary metabolome modifications after intake of almond (Prunus dulcis (Mill.) D.A. Webb) skin polyphenols

Almond, as a part of the nut family, is an important source of biological compounds, and specifically, almond skins have been considered an important source of polyphenols, including flavan-3-ols and flavonols. Polyphenol metabolism may produce several classes of metabolites that could often be more biologically active than their dietary precursor and could also become a robust new biomarker of almond polyphenol intake. In order to study urinary metabolome modifications during the 24 h after a single dose of almond skin extract, 24 volunteers (n = 24), who followed a polyphenol-free diet for 48 h before and during the study, ingested a dietary supplement of almond skin phenolic compounds (n = 12) or a placebo (n = 12). Urine samples were collected before ((-2)-0 h) and after (0-2 h, 2-6 h, 6-10 h, and 10-24 h) the intake and were analyzed by liquid chromatography-mass spectrometry (LC-q-TOF) and multivariate statistical analysis (principal component analysis (PCA) and orthogonal projection to latent structures (OPLS)). Putative identification of relevant biomarkers revealed a total of 34 metabolites associated with the single dose of almond extract, including host and, in particular, microbiota metabolites. As far as we know, this is the first time that conjugates of hydroxyphenylvaleric, hydroxyphenylpropionic, and hydroxyphenylacetic acids have been identified in human samples after the consumption of flavan-3-ols through a metabolomic approach. The results showed that this non-targeted approach could provide new intake biomarkers, contributing to the development of the food metabolome as an important part of the human urinary metabolome.

Metabolic footprinting of tumorigenic and nontumorigenic uroepithelial cells using two-dimensional gas chromatography time-of-flight mass spectrometry

In this study, gas chromatography mass spectrometry (GC-MS) and two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC-TOFMS) were employed for the metabolic footprinting of a pair of immortalized human uroepithelial cells namely HUC-1 (nontumorigenic) and HUC T-2 (tumorigenic). Both HUC-1 and HUC T-2 cell lines were cultivated in 1 mL of Ham's F-12 media. Subsequent to 48 h of incubation, 200 microL of cell culture supernatant was protein-precipitated using 1.7 mL of methanol and an aliquot of 1.5 mL of the mixture was separated, dried, trimethylsilyl-derivatized, and analyzed using GC-MS and GCxGC-TOFMS. Metabolic profiles were analyzed using multivariate data analysis techniques to evaluate the changes of the metabolomes. Both GC-MS and GCxGC-TOFMS analyses showed distinct differences in metabolic phenotypes of the normal and tumorigenic human bladder cells (partial least squares-discriminant analysis (PLS-DA) of GCxGC-TOFMS data; two latent variables, R (2) X = 0.418, R (2) Y = 0.977 and Q (2) (cumulative) = 0.852). Twenty metabolites were identified as being statistically different between the two cell types. These metabolites revealed that several key metabolic pathways were perturbed in tumorigenic urothelial cells as compared to the normal cells. Application of GCxGC-TOFMS offered several advantages compared to classical one-dimensional GC-MS which include enhanced chromatographic resolution (without increase in analytical run time), increase in sensitivity, improved identification of metabolites, and also separation of reagent artifacts from the metabolite peaks. Our results reinforced the advantages of GCxGC-TOFMS and the role of metabolomics in characterizing bladder cancer biology using in vitro cell culture models.